E:/Projects/Visual Studio/CSharp/GEP .NET Framework/GEP.Core/Float/MatingProbabilityFunctions/SogartarEllipticMappingFitnessToProbability.cs

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using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using GEP.Core.Common;

namespace GEP.Core.Float.MatingProbabilityFunctions
{
    [Serializable]
    public class SogartarEllipticMappingFitnessToProbability : MatingProbabilityFunction
    {
        public SogartarEllipticMappingFitnessToProbability(bool fitnessCloserToZeroIsBetter)
            : base(fitnessCloserToZeroIsBetter)
        {
        }

        public override void SetMatingProbability(Common.Population population)
        {
            population._totalMatingProbability = 0;
            int popSize = population.Size;
            double minFitness = double.MaxValue;
            double maxFitness = double.MinValue;
            double fitness;
            for (int i = 0; i < popSize; i++)
            {
                fitness = population._genotypes[i]._fitness;
                if (double.IsNaN(fitness) || double.IsInfinity(fitness))
                {
                    if (_fitnessCloserToZeroIsBetter)
                    {
                        population._genotypes[i]._fitness = double.MaxValue;
                        fitness = double.MaxValue;
                    }
                    else
                    {
                        population._genotypes[i]._fitness = 0;
                        fitness = 0;
                    }
                }
                else
                {
                    if (fitness < minFitness)
                        minFitness = fitness;
                    if (maxFitness < fitness)
                        maxFitness = fitness;
                }
            }
            //Subtracting from everything minFitness, so minFitness = 0.
            double doubleMidFitness = maxFitness - minFitness;
            int halfN_Population = popSize / 2;
            double medianAverageFitness = 0;
            double currentMinFitness;
            int currentMinFitnessIndex = 0;
            for (int i = 0; i <= halfN_Population; i++)
            {
                currentMinFitness = double.MaxValue;
                for (int j = 0; j < popSize; j++)
                {
                    if (population._genotypes[j]._fitness < currentMinFitness &&
                        medianAverageFitness < population[j]._fitness)
                    {
                        currentMinFitness = population._genotypes[j]._fitness;
                        currentMinFitnessIndex = j;
                    }
                }
                medianAverageFitness = currentMinFitness;
                for (int k = 0; k < popSize; k++)
                {
                    if (population._genotypes[k]._fitness == currentMinFitness && currentMinFitnessIndex != k)
                        i++;
                }
            }
            medianAverageFitness -= minFitness;    
            if (_fitnessCloserToZeroIsBetter)
            {
                double twoTimesMedianAverageFitness = 2 * (doubleMidFitness - medianAverageFitness);
                double twoTimesMaxFitnessMinusMedianAverageFitness = 2 * medianAverageFitness;
                double twoTimesMaxFitness = 2 * doubleMidFitness;
                for (int i = 0; i < popSize; i++)
                {
                    if (population._genotypes[i]._fitness == double.MaxValue)
                        population._genotypes[i]._matingProbability = 0;
                    else
                    {
                        if (medianAverageFitness <= population._genotypes[i]._fitness - minFitness)
                        {
                            population._genotypes[i]._matingProbability = 0.5 -
                                Math.Sqrt((population._genotypes[i]._fitness - minFitness - medianAverageFitness) *
                                (twoTimesMaxFitness - medianAverageFitness - population._genotypes[i]._fitness + minFitness)) /
                                twoTimesMedianAverageFitness;
                        }
                        else
                        {
                            population._genotypes[i]._matingProbability = 0.5 +
                                Math.Sqrt((medianAverageFitness - population._genotypes[i]._fitness + minFitness) *
                                (medianAverageFitness + population._genotypes[i]._fitness - minFitness)) /
                                twoTimesMaxFitnessMinusMedianAverageFitness;
                        }
                        population._totalMatingProbability += population._genotypes[i]._matingProbability;
                    }
                }
            }
            else
            {
                double twoTimesMedianAverageFitness = 2 * (medianAverageFitness);
                double twoTimesMaxFitnessMinusMedianAverageFitness = 2 * (doubleMidFitness - medianAverageFitness);
                double twoTimesMaxFitness = 2 * doubleMidFitness;
                for (int i = 0; i < popSize; i++)
                {
                    if (population._genotypes[i]._fitness == 0)
                        population._genotypes[i]._matingProbability = 0;
                    else
                    {
                        if (population._genotypes[i]._fitness - minFitness <= medianAverageFitness)
                        {
                            population._genotypes[i]._matingProbability = 0.5 -
                                Math.Sqrt((medianAverageFitness - population._genotypes[i]._fitness + minFitness) *
                                (medianAverageFitness + population._genotypes[i]._fitness - minFitness)) /
                                twoTimesMedianAverageFitness;
                        }
                        else
                        {
                            population._genotypes[i]._matingProbability = 0.5 +
                            Math.Sqrt((population._genotypes[i]._fitness - minFitness - medianAverageFitness) *
                            (twoTimesMaxFitness - medianAverageFitness - population._genotypes[i]._fitness + minFitness)) /
                            twoTimesMaxFitnessMinusMedianAverageFitness;
                        }
                        population._totalMatingProbability += population._genotypes[i]._matingProbability;
                    }
                }
            }
        }
    }
}

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